Data in the form of time-dependent sequential observations emerge in many key real-world problems, including areas such as biology, economics, weather forecasting, and audio/video processing. However, despite the ubiquity of such data, most mainstream machine learning research and algorithms have focused on the setting in which sample points are drawn i.i.d. from some (usually unknown) fixed distribution. While there exist algorithms designed to handle non-i.i.d. data, these typically assume either a specific parametric form for the data-generating distribution or an adversarial model without any distributional considerations at all. Such assumptions either undermine the complex nature of modern time series data or do not fully exploit its stochastic aspect.

The goal of this workshop is to bring together theoretical and applied researchers interested in the analysis of time series and development of new algorithms to process sequential data. This includes algorithms for time series prediction, classification, clustering, anomaly and change point detection, correlation discovery, dimensionality reduction as well as a general theory for learning and analyzing stochastic processes. We invite researchers from the related areas of batch and online learning, reinforcement learning, data analysis and statistics, econometrics, and others to contribute to this workshop.

Location and Dates:

Our workshop will take place on Friday, December 8th in Long Beach, California after the main NIPS 2017 conference.